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Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Practical Workflow from High-Throughput Genotyping to Genomic Estimated Breeding Values (GEBVs).

Felice Contaldi¹, Elisa Cappetta², Salvatore Esposito³

¹CREA Research Centre for Vegetable and Ornamental Crops, Pontecagnano Faiano, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|December 2, 2020

Summary

Genomic selection accelerates plant breeding for complex traits like stress resistance. This study provides bioinformatics tools and resources to simplify genotyping-by-sequencing (GBS) for genomic estimated breeding value (GEBV) analysis.

Keywords:

Genomic estimated breeding values (GEBVs)Machine learning Next-generation breeding Single-nucleotide polymorphisms (SNPs)Stacks rrBLUP

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Area of Science:

Plant breeding
Genomics
Bioinformatics

Background:

Climate change necessitates faster plant selection for complex traits, including biotic and abiotic stresses.
Genomic selection (GS) offers a method to accelerate breeding by using genomic estimated breeding values (GEBVs).
High-throughput genotyping (HTG), particularly genotyping-by-sequencing (GBS), captures genome-wide markers essential for explaining genetic variance.

Purpose of the Study:

To outline bioinformatics resources for implementing GBS in laboratories for GS analysis.
To provide practical scripts to facilitate GBS and GS data analysis.
To describe key factors for successful GBS and GS approaches in plant breeding.

Main Methods:

Utilizing high-throughput genotyping (HTG) systems, specifically genotyping-by-sequencing (GBS).
Applying genomic selection (GS) principles using genomic estimated breeding values (GEBVs).
Leveraging bioinformatics workflows and provided scripts for data analysis.

Main Results:

Identification of accessible bioinformatics resources for GBS and GS.
Availability of practical scripts to aid researchers in GBS and GEBV analysis.
Guidance on critical factors for effective GBS and GS implementation.

Conclusions:

Bioinformatics resources and tools can simplify GBS applications for GS in plant breeding laboratories.
Researchers without extensive bioinformatics backgrounds can perform GBS and GS analyses.
This work facilitates the acceleration of plant breeding for improved crop resilience.